Low-tech Sectors Research Articles

DIGITAL TRANSFORMATION AND THE PERFORMANCE OF SPANISH MANUFACTURING FIRMS: THE INFLUENCE OF TECHNOLOGICAL INTENSITY OF THE INDUSTRY

ABSTRACT: Digital transformation (DT) is the use and integration of digital technologies in all areas of an organization, leading to profound changes in its activities, operations and value creation for customers. It has become a matter of survival for organizations across all sectors, geographies and sizes. Despite its widespread adoption, DT success is not achieved by investing in technology alone; it is necessary to analyze the factors that influence success and understand why some companies gain a competitive advantage while others fail. This study examines three key factors of DT: business model innovation, digital technology integration and digital talent management, and their relationship to performance. In addition, we examine the environment in which each organization operates, considering the technological intensity of the sector as a potential moderating factor between these factors and performance. The study focuses on Spanish manufacturing companies, using data from the ESEE. By analyzing a panel data set of 9215 observations between 2011 and 2017, we provide evidence that the technological intensity of the sector significantly affects the relationship between business model innovation, digital technology integration, the development of new competencies and performance. The study shows that companies in medium- and low-tech sectors can benefit more quickly from investments aimed at business model innovation, digital technology integration and the development of new competencies, and achieve better short-term results compared to companies in high-tech sectors. These results suggest that, despite their limited investment in R&D, companies in medium- and low-tech sectors have resources and capabilities that compensate for their innovation activities.ABSTRACT: Digital transformation (DT) is the use and integration of digital technologies in all areas of an organization, leading to profound changes in its activities, operations and value creation for customers. It has become a matter of survival for organizations across all sectors, geographies and sizes. Despite its widespread adoption, DT success is not achieved by investing in technology alone; it is necessary to analyze the factors that influence success and understand why some companies gain a competitive advantage while others fail. This study examines three key factors of DT: business model innovation, digital technology integration and digital talent management, and their relationship to performance. In addition, we examine the environment in which each organization operates, considering the technological intensity of the sector as a potential moderating factor between these factors and performance. The study focuses on Spanish manufacturing companies, using data from the ESEE. By analyzing a panel data set of 9215 observations between 2011 and 2017, we provide evidence that the technological intensity of the sector significantly affects the relationship between business model innovation, digital technology integration, the development of new competencies and performance. The study shows that companies in medium- and low-tech sectors can benefit more quickly from investments aimed at business model innovation, digital technology integration and the development of new competencies, and achieve better short-term results compared to companies in high-tech sectors. These results suggest that, despite their limited investment in R&D, companies in medium- and low-tech sectors have resources and capabilities that compensate for their innovation activities. Keywords: Digital transformation; performance of Spanish manufacturing firms; business model innovation; digital technology integration; digital talent management; industry’s technological intensity. Keywords: Digital transformation; performance of Spanish manufacturing firms; business model innovation; digital technology integration; digital talent management; industry’s technological intensity.

The impact of digitization in manufacturing on female employment and gender wage gap

This paper comprehensively examines the impact of digitization in manufacturing on female employment and the gender wage gap, utilizing data from Chinese manufacturing enterprises. The findings indicate that digitization creates more employment opportunities for female workers, increases their employment share, and narrows the gender wage gap. Notably, the increase in female employment due to digitization is more pronounced in private enterprises, as well as in the eastern and central regions, particularly within low-tech industries. Similarly, the effect of digitization on reducing the gender wage gap is most significant in private enterprises, eastern regions, and low-tech sectors. The mechanism underlying these changes suggests that manufacturing digitization enhances female employment through increased output and productivity, thereby contributing to a reduction in the gender wage gap. This study holds significant value for advancing digitization and promoting gender equality in the labor market.

Selection and adoption of water purification technologies in the bottled water industry in India

Today, more than sixty per cent of the global bottled water manufacturing takes place in the countries of the global south. Despite being a low-tech sector, the industry is heavily influenced by the advancement and adoption of new water purification technologies and science-based regulations introduced by regulatory agencies. This paper examines the factors and mechanisms through which selection and adoption take place in this sector. The study draws upon firm-level surveys, expert interviews, ethnographic data, and select court cases and policy reports. We find that economic (firm’s size, firm’s age, and ownership structure) and socio-environmental factors (environmental subjectivities of major actors, regulatory (in-) capacity, role of supporting institutions) shape the pattern and combinations of technological adoption and technological changes at the firm level to meet the goals of “safety” and “quality”. However, the use of certain technologies are not devoid of serious environmental and public health concerns. The regulatory actors need to pay closer attention to the direct and indirect impacts of technology use on public health, the economy and the environment to make public policies more just and to meet sustainable development goals.

Financing patterns of working capital and physical investment: their effects on innovation and productivity

ABSTRACT Firms must access financial markets to surpass financial barriers limiting innovation activities. However, an overreliance on debt might moderate creativity and innovativeness. From a sample of European manufacturing firms, and applying a system of equations using GSEM, we derive a function to determine the thresholds of the optimal acquisition of working capital and physical investment. Contrasting this information with the descriptive data, firms tend to under-finance working capital, as future short-term needs are more challenging to identify when designing investment plans. Additionally, we find evidence for the heterogeneous financial needs of firms operating in high-tech as compared to low-tech sectors, as well as other differences related to firm age. Overall, this paper demonstrates the existence of an optimal proportion of working capital and physical investment that maximizes innovation activities and firm performance, deriving diminishing returns from debt financing and the complementarities between short-term and long-term financial needs.

Artificial Intelligence (AI) in the Hospitality Industry: A Review Article

Purpose – Artificial intelligence (AI) adoption is critical in the age of digital technology. This review article aims to evaluate the literature on AI in the hospitality industry. Method – A narrative synthesis was used in this review article. Moreover, the literature was reviewed systematically to explore AI in the hospitality industry. The literature and information were obtained from various books and research articles on EBSCO, Google Scholar, Scopus, Web of Science, and Science Direct. The inclusion criteria were studies that clearly defined AI in all aspects of the hospitality industry, were published and written in English and were peer-reviewed. Content analysis was employed. Results – The use of AI is a strategic and critical factor in economic development. Furthermore, AI technologies are increasingly being used as digital assistants. They help businesses in the hospitality industry in a variety of ways, including improving customer service, expanding operational capability, and lowering costs. However, there are some risks associated with AI advancements, such as job loss in low-tech sectors, loss of control due to robot autonomy, and safety, security, and privacy concerns. Conclusion – AI technologies have both positive and negative effects on the workforce and job employment in the hospitality industry. Recommendations – The recommendation is to consider a quantitative study regarding AI adoption in the hospitality industry or other sectors. Also, a qualitative approach could give a clear view of insight results for further study. Research Implications – This review article contributed to the existing literature on AI adoption in the hospitality industry. Hence, it could be used to guide future research on AI adoption in the hospitality industry. It may also aid academics in broadening their research by incorporating more potential elements. Practical Implications – This review article could lead to a better understanding of AI adoption in the hospitality industry. Moreover, it may assist business owners, managers, and marketers in the hospitality industry or any sector to achieve and enhance high business performance by implementing appropriate strategies to meet the needs and expectations of both customers and employees through the use of AI.

Measuring Regional Competitiveness: New Insights Based on the RCI

The design of a comprehensive measure of regional competitiveness has attracted much attention, with the Regional Competitiveness Index (RCI) as a benchmark. We contribute from two different perspectives. On the one hand, we test the robustness of the RCI results by modifying the dimensionality reduction technique. On the other hand, we explore the importance of two factors in the analysis of regional competitiveness: geographical distance to potential competitors (by correcting the index with the introduction of several distance measures) and technological specialization (by making a prior selection of the regions that are expected to compete mostly either in high-tech sectors or in medium- or low-tech sectors, and recalculating the index accordingly). The results show that the RCI indicator is robust to the variable reduction methods employed. Moreover, including geographical distance substantially modifies the degree of competitiveness of many regions, which we believe cannot be neglected since competition tends to be fiercer the shorter the distance between the regions involved. Finally, if regions are categorized into two groups based on their technology level, some changes in RCI rankings are very noticeable.

Determinants of Labor Productivity of Turkish Manufacturing Sectors: The Role of Global Value Chain Participation

Purpose: This paper investigates the determinants of labor productivity of Turkish manufacturing sectors by taking into account both the global value chain (GVC) participation and the research and development (R&D) expenditure for the period of 1995-2018. Methodology: To analyze the determinants of labor productivity in Turkish manufacturing sectors, we specify the labor productivity model and estimate this empirical model by the Ordinary Least Squares (OLS) and Two-Stage Least Squares (2SLS) methods. Findings: Our estimation results indicate that while simple forward GVC participation enhances the labor productivity of Turkish high-tech manufacturing sectors if they trade with developing countries, complex forward GVC participation increases productivity regardless of trading partners. For low-tech sectors, there is no significant impact of GVCs or R&D on productivity. There is no significant impact of backward GVC participation and sectoral R&D intensity on labor productivity. These significant results provide strong evidence for the importance of deeper involvement of high-tech sectors in GVCs for higher sectoral productivity. Given the strong heterogeneity in terms of sectors and trading partners, specific policies should be targeted to benefit from the productivity gains of the global value chains. Originality: This study contributes to the current studies by focusing on disaggregated measures of GVC participation indices and enlarges the empirical analysis by considering the heterogeneity in trading partners (developed and developing trading partners).

ASSESSMENT OF THE TECHNOLOGICAL LEVEL OF ENTERPRISES AND BRANCHES OF THE PROCESSING INDUSTRY OF THE UKRAINIAN

Topicality. Today, the technological level of enterprises and branches of the processing industry is the main factor in the economic development of the country's economy, as it determines the level of material and energy intensity of production, labour productivity, investment attractiveness, competitiveness of the state and living standards of the population. A lot of scientific research by foreign and domestic scholars has been devoted to assessing the technological level of enterprises and industries. Assessment of the technological level of enterprises and industries based on the indicator «share of expenditures on R&D in gross value added» does not provide a comprehensive assessment, since the technological level is determined by many factors.Aim and tasks. To carry out a comprehensive assessment of the technological level of enterprises and branches of the processing industry of Ukraine based on indicators of output, gross value added, employment and labour productivity.Materials and methods. General scientific and special research methods were used in the study: comparison, generalisation, synthesis, system analysis, logical and dialectical analysis, etc. The information base of the study includes monographs, special literature, information and analytical materials, domestic and foreign periodicals, and data from the State Statistics Committee.Research results. In assessing the technological level of enterprises and industries in the manufacturing industry, the most widely recognised approach is based on the indicator «share of expenditures on R&D in gross value added» has proposed a classification of manufacturing industries. Along with the indicator, it is proposed to use other indicators of the technological level of enterprises and industries, since the level of knowledge intensity is only one of the factors that determine the technological level. In addition, this indicator has disadvantages compared to the "intangible assets" indicator, as it reflects the level of research and development expenditures for the year, while intangible assets are the total research and development expenditures for the entire previous period. Accordingly, this indicator is more likely to be determined by the technological level of manufacturing industries. The technological level of enterprises and industries also depends on the amount of fixed assets per employee, the level of employees' qualifications, the level of production and management organisation, and other factors.The article considers two methodological approaches to assessment of the technological level of the processing industry: resource and result-based. The first approach assesses the technological level of the manufacturing industry based on the availability of various types of resources: intangible assets, fixed assets, level of education and qualification of employees, level of production and management organisation, etc. The second approach assesses the technological level of manufacturing industries based on performance indicators, such as output, gross value added, employment, and labour productivity. Assessment of the technological level of industries based on performance indicators is more adequate.In 2013-2020, the share of machine building and the high-tech sector in gross value added decreased from 25.7% to 15.8% and from 35.6% to 24.6%, respectively, while the share of the low-tech sector (low-tech and medium-tech industries) increased from 64.4% to 75.4%. The leading manufacturing industry in gross value added was food production, with a share of 23-25%, which is a low-tech industry. The share of metallurgical production (a medium-low-tech industry) was more than 10%. Between 2013 and 2020, the share of machine building and the high-tech sector in manufacturing employment decreased from 24.0% to 20.6% and from 30.7% to 27.2%, respectively, while the share of the low-tech sector increased from 69.3% to 72.8%. The manufacturing sector with the highest employment rate was food production, which increased from 19% to 21.3%.An analysis of labour productivity in manufacturing industries revealed an anomaly. The production of tobacco products (a low-tech industry) and the production of coke and petroleum products (a medium-low-tech industry) are significantly ahead of high-tech industries in terms of labour productivity. Between 2013 and 2020, labour productivity in high-tech and medium-high-tech manufacturing industries decreased from USD 66.7 thousand (purchasing power parity, constant 2017 prices in thousands of international dollars) to USD 50.1 thousand and from USD 42.0 thousand to USD 37.5 thousand, respectively. And in medium- and low-tech industries, it increased from USD 37.8 thousand to USD 52.8 thousand and from USD 39.5 thousand to USD 41.5 thousand. Thus, there was an inversion: while medium-low-tech industries had the lowest labour productivity in 2013 ($37.8 thousand), in 2020 they had the highest labour productivity ($52.8 thousand).Conclusion. A comprehensive assessment of the technological level of enterprises and branches of the processing industry of Ukraine is carried out on the basis of indicators of output, gross value added, employment and labour productivity. The results obtained show that in 2013-2020, Ukraine continued the process of deindustrialisation of the country's economy and a decrease in the technological level of the processing industry.

Decision Making for Knowledge Management in the Tequila Sector: A Fuzzy Logic Model

Knowledge management creates value for organizations, allowing them to be more innovative, productive, and competitive if that knowledge is used appropriately. Through this management, vital information is created and disseminated systematically and efficiently, and at the same time, knowledge learning is adopted, transformed, shared, and applied. This research analyzes decision-making for knowledge management in a mature low-tech sector, such as Tequila in Mexico. At the same time, it generates a predictive model of knowledge management that allows innovation in this sector by combining knowledge of modern technologies and ancestral knowledge in manufacturing the product, along with providing support to public policymakers and decision-makers to support small producers and rural communities. The methodological strategy used is an expert system through fuzzy logic, starting from a data set based on the patterns found in a Bayes network. The results show that the most relevant variables in decision-making for knowledge management in the Tequila sector are modern technologies, ancestral knowledge, and the Denomination of Origin. Under the above, it could be inferred that the ancestral knowledge variable is the most influential in achieving high values in managing knowledge management -the output variable preserving the value of a product with a designation of origin.

Unleashing the value of artificial intelligence in the agri-food sector: where are we?

PurposeThis study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions.Design/methodology/approachThis study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions.FindingsSix clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector.Originality/valueThis study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector.

University-industry Collaboration and Innovation in Low-tech Industries: the Case of Brazil

Abstract Despite the importance of low-tech manufacturing sectors for national economies, previous studies on UIC benefits for the firms were conducted mostly in high-tech sectors, making the knowledge on the impact of UIC on innovation in low-tech sectors unexplored. The present research aims to identify combinations of UIC s that may lead the low-tech firms to high innovation performance. By using the csQCA technique applied to secondary data from the BR Survey, which is the largest UIC databank in Brazil, the researchers identified that the most intense collaborations in terms of knowledge and resource exchange, such as development-oriented and research-oriented, are among the most beneficial for firms’ innovativeness. Diffusion-oriented UIC does not contribute to innovation, unless it is combined with both research-oriented and development-oriented UIC s. Finally, the csQCA suggests that in most cases the firms should focus on engaging in one specific UIC type rather than split its effort and resources among a mix of UIC. The findings amplify comprehension of the range of impacts that entrepreneurial universities promote in low-tech sector, creating both theory and policy implications.

Barriers as moderators in the innovation process

Abstract This article investigates the effect of financial and non-financial barriers on innovativeness. Using microdata from Brazil, it provides a rare detailed empirical investigation of this type in developing countries. The analysis is based on a novel conceptual framework of the moderating role of barriers to innovation. Research and development expenditure and informal methods of intellectual property protection are the innovation determinants least affected by obstacles to innovation. This is in sharp contrast to company size, whose effect appeared quite sensitive to barriers of all kinds. Disembodied and embodied knowledge outsourcing interact differently with different constraints: while the former appeared helpful in working around different types of barriers in low-tech sectors, the latter was more useful in addressing financial constraints in high-tech sectors. Finally, cooperation with other firms was negatively affected by obstacles when firms seek more radical innovations, whereas cooperation with research and education organizations proved attractive for companies facing organizational constraints.

Factors affecting business performance of e-mobility companies in Korea

The emergence of e-mobility is leading to significant changes in the field of mobility. It has the potential to address the issue of climate change and provide personalized technology to various consumers through the use of intelligent technology. As e-mobility is an emerging industry, technological innovation and the survival and growth of companies within it are crucial. Unlike the automobile industry, e-mobility industry is a low-tech sector and low entry barriers, and has characteristics that are composed of SMEs. Nevertheless, there is a lack of research on the factors that affect the performance of e-mobility companies. This study aimed to identify the performance factors of e-mobility companies and analyze their impact on growth and profits. The study was conducted on the e-mobility industry in Korea, where advanced ICT and transportation technology have enabled the industry to make significant progress. The study found that higher investment in R&D for technological innovation led to better growth potential and profitability for e-mobility companies. It also revealed that improving synergies at the regional level cluster policy is crucial for performance, rather than just temporary policy support at the national level.

Modelling sustainable development of industrial systems

This paper describes a methodological approach that has been developed by the authors for assessment and modelling of sustainable development of industrial systems. Based on experimental testing of this methodological approach applied to the Republic of Tatarstan as an exemplary region, the authors have determined the industry-specific trends in terms of sustainable development of industrial complexes, which are regarded as the dominant elements of the sustainable economic growth process with prevalence of the upstream and high-tech sectors of the economy, along with unstable industries represented by low-tech sectors, as well as the so-called economic growth reinforcing industries. For each identified industrial group, prospects of growth and development have been evaluated as well. In this study the authors have also developed the sustainable growth models involving the models of production functions relevant to definite industrial groups (clusters). The authors furthermore have identified the key factors that are instrumental in better functioning of such groups (clusters). In the course of the production functions modelling, a closer connection between the economic potential and human capital has been tracked, which is particularly exemplified by two out of the three clusters being under review in this paper, namely, in volatile sectors of the economy, as well as in the economic growth reinforcing industries, but with one exception, however, involving the cluster of the economic development dominant elements – the upstream sector of the economy. As such, based on the resulting data, this study sets out the proofs of the “growing heaviness” of the economic development process due to the capital-intensive elements of the upstream sector of the economy, as opposed to a more flexible factor – the human capital.

ТЕХНОЛОГІЇ ЦИФРОВІЗАЦІЇ БІЗНЕС-ПРОЦЕСІВ ЯК ІНСТРУМЕНТИ ІННОВАЦІЙНОГО РОЗВИТКУ ПІДПРИЄМСТВ

This article examines the digitalization technologies of business processes, which in modern conditions are tools for the innovative development of domestic enterprises. The concepts of "technology", "digitalization technologies", and "digitalization technologies of business processes of enterprises" are characterized. The types of technologies are defined, depending on their connection with production and information and communication support, as well as according to their life cycle. A comparative analysis of the dynamics and structure of different levels of the technological sector over the past ten years has been conducted, which indicates a decrease in the share of the low-tech sector of the Ukrainian economy and a simultaneous increase in the specific weight of the high-tech sector. Goals have been set for the use of digitalization technologies for business processes in the context of innovative enterprise development, and tasks that can be solved with the help of these technologies have been defined, in particular: improving interaction with suppliers and buyers; increasing the flexibility and adaptability of business processes, reducing the time for their execution; obtaining economic, social, technological and environmental effects. The directions of research on digitalization issues at the current stage by scientists and practitioners, representatives of various fields of activity (legal, technical, economic, etc.) are outlined. An analysis of the most widespread digitalization technologies at the current stage has been carried out (Internet of Things (IoT), Internet of Things, Big Data, Cloud Computing, Cloud Computing (AI), Agile, Chat Bot, Blockchain, etc.), the spheres of their application are defined, as well as the advantages and disadvantages of each of them. Features of the application of digitization technologies in various spheres and branches of activity: banking, trade, industry, agriculture, transport and logistics, etc. Conclusions were made about the need to use and constantly improve digitization technologies to accelerate the digital transformation of business processes of enterprises, which will contribute to their innovative development and create an opportunity to attract investments to the domestic economy.

Data-driven decision support tool for production planning: a framework combining association rules and simulation

Nowadays, guaranteeing the highest product variety in the shortest delivery time represents one of the main challenges for most of industries. The dynamic contexts where they have to compete push them to quickly readapt their processes, increasing the need for reactive decision-support tools to identify targeted actions to improve performance. Starting from the analysis of existing decision-support tools separately adopting simulation or data mining techniques, a framework that combines Association Rule Mining (ARM) and simulation has been developed to capitalize on the benefits brought by both techniques. On the one hand, ARM supports companies in identifying the main criticalities that slow down production processes, such as different causes of stoppage, giving a priority ranking of interventions. On the other hand, data-driven simulation is used to validate the ARM results and to conduct scenario analyses to compare the KPIs values resulting from different configurations of the production processes. Once the best-impacting mitigating actions have been implemented, the proposed framework can be iteratively used to define an updated set of intervention areas to enhance, promoting continuous improvement. This data-driven approach represents the key value of the framework, guaranteeing its easy-to-readapt and iteratively application. Theoretical contributions refer to the use of simulation with ARM not only to validate relations but to perform scenario analyses in an iterative way, as well as to the novelty application in a low-tech sector. From a practical point of view, a case study in the fashion industry demonstrates the usability and reliability of the proposed framework.

When are acquisitions a viable catch-up strategy? Some empirical evidence from China

How emerging-market firms can catch up with forerunners from advanced economies is a key issue in the economic and technology literature. Research has suggested that acquisitions are a viable tool for firms in emerging markets to reduce the productivity gap with global leaders, but the empirical evidence on this matter is still far from conclusive. Contributing to this debate, this paper examines the impact of cross-border vs. domestic acquisitions on the labor productivity of firms across different sectoral environments. Studying the acquisitions pursued by Chinese listed firms over one decade, we find that cross-border acquisitions are positively associated with firms' labor productivity and that this effect is particularly strong in high-tech sectors and among leading firms. We also find that domestic acquisitions are positively associated with firms’ labor productivity and that this effect is particularly strong in low-tech sectors and among laggards. We further investigate the mechanisms underlying the acquisition–productivity link and contend that “technological innovation” is the primary mechanism by which acquisitions enhance firm productivity in high-tech sectors, whereas “enhancing operating efficiency” is the primary mechanism by which acquisitions enhance firm productivity in low-tech sectors.

The relationship between R&D knowledge spillovers and employment entry

Recent approaches to entrepreneurship seek to explain regional heterogeneity by exploring the link between knowledge endowment and new firm creation. There are two main gaps in this stream of research. First, entrepreneurship tends to be considered in terms of entry rates rather than in terms of job creation. Second, most empirical studies focus on relatively large geographical areas and overlook the distance at which knowledge externalities dissipate. The present paper exploits data on firms based in the Emilia-Romagna region (Italy) to show that private R&D spillovers are positively associated with the size at entry of innovative firms only for those located close to the R&D activities and that these spillovers dissipate at a few kilometres from the R&D source. Non-linearities are detected only for low-tech sectors.

From growth goals to proactive organizational resilience: first evidence in women-led and non-women-led Italian wineries

In a fast-changing world, characterized by evenly unexpected challenges and shocks, being resilient is a crucial aspect for every organization. Drawing from the goal setting theory and the double standards of competence perspective, this study aims at understanding the antecedents of organizational proactive resilience. More precisely, it looks at the impact of quantitative and qualitative organizational growth goals on proactive resilience, distinguishing between women-led and non-women-led firms. Based on a unique sample of 167 Italian wineries (67 women-led and 100 non-women-led), this paper tests this theoretical model using path analysis techniques. The wine sector is a particularly interesting context to study the phenomenon due to its exposure to natural disasters, new consumers’ behaviours that are requiring firms to continuously innovate and differentiate in a traditionally low-tech sector, but also changes happening at wineries’ management level. In fact, the sector has been traditionally male dominated, but women are increasingly taking the lead. The findings indicate that growth goals differently contribute to proactive organizational resilience, but their effects vary in women- and non-women-led businesses. Specifically, these results suggest that in women-led wineries, proactive organizational resilience depends on quantitative growth goals while in non-women-led wineries businesses this depends on qualitative growth goals.

Factors determining the location decision: Analysis of location choice preferences of the ICI-1000 companies with the nested logit model

The aim of this study is to analyze the location decisions of the manufacturing industry companies in the ICI Top 500 and Second Top 500 Industrial Enterprises (ICI-1000) for the year 2018. The location choice model developed accordingly is based on the assumption that companies choose the location of their production facilities with “the goal of profit maximization, and that this decision is influenced by both internal (company-specific) and external (sectoral and regional) factors”. The empirical analysis is conducted utilizing the nested logit estimation method and a large data set containing information on 909 manufacturing industry companies among ICI-1000, sub-sectors and location alternatives. The results support the views of Neoclassical and Institutional location approaches. The location decisions of the companies are affected by the characteristics specific to the company, sector and location. The ICI-1000 companies in the study tend to locate in areas with high market power and market growth, qualified and abundant labor, high sectoral growth and diversity and good geographical and physical conditions. The impact of these factors on company location preferences varies depending on the technological intensity of the industry in which they operate (high/low). Companies operating in high-tech (high, medium-high, medium-low) sectors choose places with a diversified and deepened labor pool, sectoral diversity and knowledge diffusion. On the other hand, the effect of specialization that emerged with localization economies is crucial in the location decisions of companies in low-tech sectors.